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26E074NIO - Algorithms for Nondestructive Evaluation of Objects

Course specification
Course title Algorithms for Nondestructive Evaluation of Objects
Acronym 26E074NIO
Study programme Electrical Engineering and Computing
Module Information and Communication Technologies - Audio and Video Technologies, Information and Communication Technologies - Internet and Mobile Communications, Information and Communication Technologies - Microwave Technology
Type of study bachelor academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
    ESPB 6.0 Status elective
    Condition
    The goal Course objective is to introduce students to modern algorithms for image formation of objects based on sensor data, as well as to develop practical skills for their application in engineering practice and research.
    The outcome Students will be able to understand and apply different algorithms for image reconstruction from sensor measurements across a broad frequency range to solve practical engineering problems.
    Contents
    Contents of lectures Introduction to the object examination using sensor measurements.The relationship between the physical properties of objects and sensor measurements. Adaptation of models depending on frequency. Numerical approximation of models. Inverse problems and solution stabilization. Fundamental algorithms for image reconstruction. Tikhonov regularization. Gauss-Newton method. Quality assessment metrics
    Contents of exercises Lectures are complemented by in-class demonstrations of algorithms using software tools such as MATLAB and Python, applied to examples relevant to engineering practice. As part of the course, students also work on individual projects, enabling a deeper understanding and practical application of the covered methods.
    Number of hours per week during the semester/trimester/year
    Lectures Exercises OTC Study and Research Other classes
    3 1 1
    Methods of teaching Lectures, demonstrations, homework, and individual projects.
    Knowledge score (maximum points 100)
    Pre obligations Points Final exam Points
    Activites during lectures Test paper 30
    Practical lessons 40 Oral examination
    Projects 30
    Colloquia
    Seminars